Adaptive Filtering Primer with MATLAB

ISBN-13: 9780849370434 / Angielski / Miękka / 2006 / 240 str.

Alexander D. Poularikas; Zayed M. Ramadan

Opis

Because of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level.
Adaptive Filtering Primer with MATLAB(r) clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB(r) functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage.
With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB(r) is an ideal companion for quick reference and a perfect, concise introduction to the fiel

Spis treści

INTRODUCTION
Signal Processing
An Example
Outline of the Text
DISCRETE-TIME SIGNAL PROCESSING
Discrete Time Signals
Transform-Domain Representation of Discrete-Time Signals
The Z-Transform
Discrete-Time Systems
Problems
Hints-Solutions-Suggestions
RANDOM VARIABLES, SEQUENCES, AND STOCHASTIC PROCESSES
Random Signals and Distributions
Averages
Stationary Processes
Special Random Signals and Probability Density Functions
Wiener-Khinchin Relations
Filtering Random Processes
Special Types of Random Processes
Nonparametric Spectra Estimation
Parametric Methods of power Spectral Estimation
Problems
Hints-Solutions-Suggestions
WIENER FILTERS
The Mean-Square Error
The FIR Wiener Filter
The Wiener Solution
Wiener Filtering Examples
Problems
Hints-Solutions-Suggestions
EIGENVALUES OF RX - PROPERTIES OF THE ERROR SURFACE
The Eigenvalues of the Correlation Matrix
Geometrical Properties of the Error Surface
Problems
Hints-Solutions-Suggestions
NEWTON AND STEEPEST-DESCENT METHOD
One-Dimensional Gradient Search Method
Steepest-Descent Algorithm
Problems
Hints-Solutions-Suggestions
THE LEAST MEAN-SQUARE (LMS) ALGORITHM
Introduction
Derivation of the LMS Algorithm
Examples Using the LMS Algorithm Equation
Performance Analysis of the LMS Algorithm Equation
Learning Curve
Complex Representation of LMS Algorithm
Problems
Hints-Solutions-Suggestions
VARIATIONS OF LMS ALGORITHMS
The Sign Algorithms
Normalized LMS (NLMS) Algorithm
Variable Step-Size LMS (VSLMS) Algorithm
The Leaky LMS Algorithm
Linearly Constrained LMS Algorithm
Self-Correcting Adaptive Filtering (SCAF)
Transform Domain Adaptive LMS Filtering
Error Normalized LMS Algorithms
Problems
Hints-Solutions-Suggestions
LEAST SQUARES AND RECURSIVE LEAST-SQUARES SIGNAL PROCESSING
Introduction to Least Squares
Least-Square Formulation
Least-Squares Approach
Orthogonality Principle
Projection Operator
Least-Squares Finite Impulse Response Filter
Introduction to RLS Algorithm
Problems
Hints-Solutions-Suggestions
ABBREVIATIONS
BIBLIOGRAPHY
APPENDIX A: MATRIX ANALYSIS
INDEX

O autorze

Alexander D. Poularikas, Zayed M. Ramadan